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High-Throughput Computational Screening of Electrocatalysts by Active Machine Learning for First-principles Database
- Multiscale Molecular Dynamics Simulations Driven by Machine Learning API
- Development and Application of Artificial Convolutional Neural Networks for Efficient Energy Materials and Processes
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Molecular-level Mechanism Elucidation for Reactions in Energy Materials
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Development of Ab-initio Thermodynamic and Kinetic Theories to Predict Material Properties
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Computational Radioactive Chemistry Applied to Spent Nuclear Fuel Management and Toxic Gas Removal
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Discovery of Cost-effective and Active Nanoparticles for Electrochemically Functional Materials
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Design of the Next Generation Battery Materials
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Integration of Quantum Mechanics and Machine Learning to Material Design Process
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